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Title:Statistical methods for modeling RNA-Seq short-read data
Author(s):Dalpiaz, David
Director of Research:Ma, Ping
Doctoral Committee Chair(s):Ma, Ping
Doctoral Committee Member(s):Douglas, Jeffrey A.; Simpson, Douglas G.; Zhong, Wenxuan
Department / Program:Statistics
Degree Granting Institution:University of Illinois at Urbana-Champaign
Gene expression
Penalized likelihood
Differential expression
Abstract:This thesis explores various methods for analyzing data generated using the next-generation sequencing technology, RNA-Seq. Two methods are developed which attempt to accurately calculate RNA expression, the first using a penalized regression approach to remove bias based on nucleotide composition, as well as a second which demonstrates the use of variation as an estimate of gene expression. Another method is developed which utilizes RNA-Seq gene expression data to identify genomic regulatory elements using a semi-parametric model with multiple responses considered simultaneously. Lastly, a method is established which identifies differentially expressed genes in timecourse data using a functional ANOVA mixed-effect model.
Issue Date:2014-09-16
Rights Information:Copyright 2014 David Dalpiaz
Date Available in IDEALS:2014-09-16
Date Deposited:2014-08

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